Ultrasound devices work on the basis of non-invasive transmission and reception of high frequency mechanical sonic waves.
The transducers of such devices transmit the ultrasound waves to the medium under scan.
The waves interact with the underlying structures in the medium, through scattering and reflections.
The underlying structures include, for example, a structure inside the medium rather than the surface thereof. The underlying structures include, for example, blood vessel in the human body.
The scattered and reflected waves contain the useful information of the underlying structures, which are received by the transducers and processed by the ultrasound devices to be presented to users.
FIG. 8 illustrates ultrasound RF signals.
One of the most basic types of data that an ultrasound device obtains from the reflected waves is Radiofrequency Signal (RF) signals (FIG. 8).
It is the direct translation of the received waves from their analog form to their digital form.
From ultrasound RF signals, other types of data can be derived, such as brightness mode (B-mode) images, Doppler images, etc. with different applications.
One of many applications is to analyze the motions of the underlying structures in the scanned medium.
The Doppler Effect is utilized as a simple method to deduce the direction and strength of structural motion.
However, the Doppler Effect has very limited accuracy.
For applications such as monitoring of blood flow in medical ultrasound, wherein the accuracy is not strictly required, Doppler ultrasound is suitable.
However, for applications which require a much higher level of accuracy, wherein the structures are small and their movement is minuscule, a much more sensitive technique is demanded.
Recently, ultrasound elastography is a new application wherein structure's displacements can be used to deduce the structure's elasticity.
These displacements need to be accurately estimated from the received ultrasound signals to provide an accurate estimation of elasticity.
Apart from resolution (i.e. the ability to estimate minuscule displacements), a higher level of accuracy is also required for displacement estimation using ultrasound.
In many prior arts, displacements are estimated from ultrasound B-mode images.
However, the quality of the estimated displacements depends largely on the quality and the resolution of the B-mode images.
For most ultrasound devices, the resolution of B-mode images does not allow displacements of micrometer order to be estimated.
Some other works focus on estimating displacements directly from the received RF signals.
Cross-correlation is one of the most common techniques, as in [1].
However, cross-correlation is computationally intensive, and it can only estimate displacements corresponding to multiple of sampling points.
Displacements of micrometer order usually correspond to a small fraction of one sampling interval.
Thus, the cross-correlation fails to estimate the displacements.
There are works which rely on signal interpolation to estimate displacements in such situation.
However, this will increase the processing time, and the estimation quality depends on the interpolation method.
Auto-correlation relies on the phase information of the quadrature demodulated signals (a.k.a. base-band signals) of the received RF signals.
This method has the advantage of being able to estimate displacement corresponding to sub-sample of RF signals, which is described in [2].
However, it is highly prone to noise, and it's affected by amplitude modulation effect.
More specifically, the estimated displacements are biased toward the region with high signal power.
To overcome the effect of noise, a larger set of samples can be chosen to perform auto-correlation, which reduces the ability to estimate more detailed displacements.
Some methods are developed to overcome the inaccuracy of the estimated displacements from the above techniques.
‘Coarse to fine’ approach makes use of different windowing regions for different stage of estimation, so that the first stage provides a coarse estimation of displacement, and the second stage gives a finer estimation to improve the accuracy, as in [3].
However, no method for evaluating estimation quality is available.
In some other works, signal warping based on the estimation result of the first stage is used to improve the accuracy of this result by conducting a second estimation stage, and combine the results, as in [1].
Other displacement correcting methods are also available as in [2].
These methods carry a high risk of divergence; hence the number of stages has to be constrained.
In addition, there are no methods to guide the convergence, and to overcome the effect of noise and bias caused by uneven distribution of RF signal power.
An improvement to these methods is needed, to give a more comprehensive iterative estimation method, which provides an indication of displacement estimation quality, guides the convergence of the iterative estimation without having to limit the number of estimation stages, and gives a high accuracy even for minuscule displacements.
The techniques disclosed in Patent Literatures 1 to 6 are known as the conventional examples.